‘There is no such place as away’: residential deconstruction as a method for waste diversion in Canada’s built environment
Bibliographic record
Abstract
Waste diversion and reduction continues to be a prominent discussion among Canadian municipalities as we collectively recognize the impact that waste production has on the environment and our future, especially in the context of climate change. Much of the focus in this regard has been on individual waste generation and reduction and the “zero-waste” movement, with less focus on construction, renovation, and demolition (CRD) waste. Research shows that CRD waste contributes between 27% and 40% of total municipal solid waste in Canada and it is estimated that the CRD sector is responsible for 40% of raw material consumption in North America. With an estimated potential of 95% of CRD materials being available for salvage, reuse, repurposing, and recycling, there is a lot of opportunity for growth in responsible CRD waste management. My research shows that deconstruction, rather than demolition of buildings, is an important next step in waste diversion for Canadian municipalities and the waste generated from CRD presents an opportunity to recover a significant amount of resources. This research explores the barriers for deconstruction programs and policies for large, Canadian municipalities, how to overcome those barriers, and establishes a framework for moving forward in a municipal setting, working with the City of Edmonton for a real-world application. The results show that deconstruction has a small foothold in Canada and the US, but there are some leading-edge and developing examples. My framework builds on these and offers a path for actioning residential building deconstruction that can have a significant impact on reducing CRD waste going to landfills.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.038 | 0.030 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".